Patent US10083233

Video processing for motor task analysis
[+] Show Details

US10083233

1. A computer-implemented method comprising: (8) (5)

receiving a video depicting at least part of a user performing a motor task;

redacting features from the video which may identify the user;

inputting the video to a trained machine learning system, the trained machine learning system having been trained to detect multiple sequences of location-dependent local motion features of videos which discriminate among a plurality of classes of the motor task; and

receiving, from the trained machine learning system, a label identifying a motor task class of the plurality of classes, the plurality of classes including

an indication of a performance level of the motor task based on at least one of the multiple sequences of location-dependent local motion features in the video detected by the trained machine learning system.

2. A method as claim 1 recites, wherein the local motion features comprise velocity or acceleration features. (1) (0)

5. A method as claim 1 recites further comprising calculating motion descriptors for pairs of frames of the video, and wherein inputting the video to the trained machine learning system comprises inputting the motion descriptors. (4) (0)

10. A method as claim 1 recites further comprising pre-processing the video prior to inputting the video to the trained machine learning system, at least by one or more of scaling, centering, or carrying out foreground extraction. (0)

11. A method as claim 1 recites, wherein: (0) (2)

12. A method as claim 1 recites further comprising training the machine learning system using videos of people performing the motor task, where the videos are labeled with labels indicating which of a plurality of possible classes the motor task belongs to, and where the videos are of different lengths. (0)

13. A method as claim 1 recites further comprising inputting the video to a trained machine learning system comprising one or more of: a random decision forest, a jungle of directed acyclic graphs, or an ensemble of support vector machines. (0)

14. A method as claim 1 recites further comprising inputting the video to a trained machine learning system comprising an ensemble of support vector machines, a support vector machine of the ensemble including a split node of a binary decision tree. (0)

15. A method as claim 1 recites further comprising inputting the video to a trained machine learning system comprising an ensemble of support vector machines, individual ones of the support vector machines having been trained using fixed length feature descriptors comprising randomized location-dependent local motion features computed from labeled training videos. (0)

16. A computer storage media storing instructions comprising: (0) (4)

instructions to receive a video depicting at least part of a person or animal performing a motor task;

instructions to input the video to a trained machine learning system, having been trained to detect multiple sequences of location-dependent local acceleration features of videos which discriminate among a plurality of classes of the motor task, the local acceleration features calculated by taking into account frequency of change of direction of rate of change of optical flow values of a sub-volume of the video; and

instructions to receive, from the trained machine learning system a label identifying a motor task class of the plurality of classes, wherein the plurality of classes includes

17. A motor-task classifier comprising: (2) (2)

a trained machine learning system, having been trained to detect multiple sequences of location-dependent local motion features of videos which discriminate among a plurality of classes of a motor task, the training comprising optimizing a criteria based at least in part on a duration of a sub-volume; and

a processor arranged to: compute motion descriptors from a video;

input the motion descriptors to the trained machine learning system; and

receive, from the trained machine learning system, a label identifying a motor task class of the plurality of classes, wherein the plurality of classes includes

18. A motor-task classifier as claim 17 recites, wherein the local motion features include velocity or acceleration features. (0)

19. A motor-task classifier as claim 17 recites, wherein computing motion descriptors from the video includes calculating motion descriptors for pairs of frames of the video. (0)

View Abstract and Specification Size

PDF with Images and Document Face >

Full Text Publication >

Patent US10083233

Video processing for motor task analysis

Patent Matrix® Search